{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mchrist/Documents/Research/tsfresh/venv/lib/python2.7/site-packages/IPython/html.py:14: ShimWarning: The `IPython.html` package has been deprecated since IPython 4.0. You should import from `notebook` instead. `IPython.html.widgets` has moved to `ipywidgets`.\n",
      "  \"`IPython.html.widgets` has moved to `ipywidgets`.\", ShimWarning)\n",
      "/Users/mchrist/Documents/Research/tsfresh/venv/lib/python2.7/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  from pandas.core import datetools\n",
      "/Users/mchrist/Documents/Research/tsfresh/venv/lib/python2.7/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pylab as plt\n",
    "import seaborn as sns\n",
    "from tsfresh.examples.robot_execution_failures import download_robot_execution_failures, load_robot_execution_failures\n",
    "from tsfresh import extract_features, extract_relevant_features, select_features\n",
    "from tsfresh.utilities.dataframe_functions import impute\n",
    "from tsfresh.feature_extraction import ComprehensiveFCParameters\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "\n",
    "#http://tsfresh.readthedocs.io/en/latest/text/quick_start.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>time</th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "      <th>f</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>63</td>\n",
       "      <td>-3</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "      <td>-3</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>61</td>\n",
       "      <td>-3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>63</td>\n",
       "      <td>-2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>63</td>\n",
       "      <td>-3</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  time  a  b   c  d  e  f\n",
       "0   1     0 -1 -1  63 -3 -1  0\n",
       "1   1     1  0  0  62 -3 -1  0\n",
       "2   1     2 -1 -1  61 -3  0  0\n",
       "3   1     3 -1 -1  63 -2 -1  0\n",
       "4   1     4 -1 -1  63 -3 -1  0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "download_robot_execution_failures()\n",
    "df, y = load_robot_execution_failures()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
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huLAA9qqq8+4rj4iAqmcvKBMSoUxMBOwOVG/ZBMOuLBh2ZUERH4/w0WMROnQYP9Ujj2NI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\nJBKJpA4pkiUSiUQikUgkkjr+f4SKhzWO002cAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1102c8450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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sEREREekZMio8A8yalLhoyj+XbU152bpUt4iIiIgcScaF55K+OYwb3pePd9Sx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\nIiIiIiIDpPAsIiIiIjJA/x/nitj20a9B2QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115934190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df.id == 3][['time', 'a', 'b', 'c', 'd', 'e', 'f']].plot(x='time', title='Success example (id 3)', figsize=(12, 6));\n",
    "df[df.id == 20][['time', 'a', 'b', 'c', 'd', 'e', 'f']].plot(x='time', title='Failure example (id 20)', figsize=(12, 6));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "extraction_settings = ComprehensiveFCParameters()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Feature Extraction: 100%|██████████| 6/6 [00:17<00:00,  2.84s/it]\n",
      "WARNING:tsfresh.utilities.dataframe_functions:The columns ['a__spkt_welch_density__coeff_8' 'c__spkt_welch_density__coeff_8'\n",
      " 'b__spkt_welch_density__coeff_8' 'e__spkt_welch_density__coeff_8'\n",
      " 'd__spkt_welch_density__coeff_8' 'f__spkt_welch_density__coeff_8'] did not have any finite values. Filling with zeros.\n"
     ]
    }
   ],
   "source": [
    "#column_id (str) – The name of the id column to group by\n",
    "#column_sort (str) – The name of the sort column.\n",
    "X = extract_features(df, \n",
    "                     column_id='id', column_sort='time',\n",
    "                     default_fc_parameters=extraction_settings,\n",
    "                     impute_function= impute)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style>\n",
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       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a__mean_abs_change_quantiles__qh_1.0__ql_0.8</th>\n",
       "      <th>a__percentage_of_reoccurring_values_to_all_values</th>\n",
       "      <th>a__mean_abs_change_quantiles__qh_1.0__ql_0.2</th>\n",
       "      <th>a__mean_abs_change_quantiles__qh_1.0__ql_0.0</th>\n",
       "      <th>a__large_standard_deviation__r_0.45</th>\n",
       "      <th>a__absolute_sum_of_changes</th>\n",
       "      <th>a__mean_abs_change_quantiles__qh_1.0__ql_0.4</th>\n",
       "      <th>a__mean_second_derivate_central</th>\n",
       "      <th>a__autocorrelation__lag_4</th>\n",
       "      <th>a__binned_entropy__max_bins_10</th>\n",
       "      <th>...</th>\n",
       "      <th>f__fft_coefficient__coeff_0</th>\n",
       "      <th>f__fft_coefficient__coeff_1</th>\n",
       "      <th>f__fft_coefficient__coeff_2</th>\n",
       "      <th>f__fft_coefficient__coeff_3</th>\n",
       "      <th>f__fft_coefficient__coeff_4</th>\n",
       "      <th>f__fft_coefficient__coeff_5</th>\n",
       "      <th>f__fft_coefficient__coeff_6</th>\n",
       "      <th>f__fft_coefficient__coeff_7</th>\n",
       "      <th>f__fft_coefficient__coeff_8</th>\n",
       "      <th>f__fft_coefficient__coeff_9</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>-0.038462</td>\n",
       "      <td>0.17553</td>\n",
       "      <td>0.244930</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>-0.038462</td>\n",
       "      <td>0.17553</td>\n",
       "      <td>0.990835</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>0.744415</td>\n",
       "      <td>1.273659</td>\n",
       "      <td>-0.809017</td>\n",
       "      <td>1.373619</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.309017</td>\n",
       "      <td>-1.391693</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>-0.038462</td>\n",
       "      <td>0.17553</td>\n",
       "      <td>0.729871</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>-0.424716</td>\n",
       "      <td>0.878188</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.851767</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-2.805239</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.214286</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>-0.038462</td>\n",
       "      <td>0.17553</td>\n",
       "      <td>1.322950</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-1.078108</td>\n",
       "      <td>3.678858</td>\n",
       "      <td>-3.618034</td>\n",
       "      <td>-1.466977</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-1.381966</td>\n",
       "      <td>-0.633773</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.928571</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.038462</td>\n",
       "      <td>0.17553</td>\n",
       "      <td>1.020037</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-3.743460</td>\n",
       "      <td>3.049653</td>\n",
       "      <td>-0.618034</td>\n",
       "      <td>1.198375</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>1.618034</td>\n",
       "      <td>-0.004568</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1332 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    a__mean_abs_change_quantiles__qh_1.0__ql_0.8  \\\n",
       "id                                                 \n",
       "1                                       0.142857   \n",
       "2                                       0.000000   \n",
       "3                                       0.000000   \n",
       "4                                       0.000000   \n",
       "5                                       2.000000   \n",
       "\n",
       "    a__percentage_of_reoccurring_values_to_all_values  \\\n",
       "id                                                      \n",
       "1                                            0.933333   \n",
       "2                                            1.000000   \n",
       "3                                            0.933333   \n",
       "4                                            1.000000   \n",
       "5                                            0.866667   \n",
       "\n",
       "    a__mean_abs_change_quantiles__qh_1.0__ql_0.2  \\\n",
       "id                                                 \n",
       "1                                       0.142857   \n",
       "2                                       0.400000   \n",
       "3                                       0.714286   \n",
       "4                                       0.800000   \n",
       "5                                       0.916667   \n",
       "\n",
       "    a__mean_abs_change_quantiles__qh_1.0__ql_0.0  \\\n",
       "id                                                 \n",
       "1                                       0.142857   \n",
       "2                                       1.000000   \n",
       "3                                       0.714286   \n",
       "4                                       1.214286   \n",
       "5                                       0.928571   \n",
       "\n",
       "    a__large_standard_deviation__r_0.45  a__absolute_sum_of_changes  \\\n",
       "id                                                                    \n",
       "1                                   0.0                         2.0   \n",
       "2                                   0.0                        14.0   \n",
       "3                                   0.0                        10.0   \n",
       "4                                   0.0                        17.0   \n",
       "5                                   0.0                        13.0   \n",
       "\n",
       "    a__mean_abs_change_quantiles__qh_1.0__ql_0.4  \\\n",
       "id                                                 \n",
       "1                                       0.142857   \n",
       "2                                       0.400000   \n",
       "3                                       0.714286   \n",
       "4                                       0.800000   \n",
       "5                                       0.916667   \n",
       "\n",
       "    a__mean_second_derivate_central  a__autocorrelation__lag_4  \\\n",
       "id                                                               \n",
       "1                         -0.038462                    0.17553   \n",
       "2                         -0.038462                    0.17553   \n",
       "3                         -0.038462                    0.17553   \n",
       "4                         -0.038462                    0.17553   \n",
       "5                          0.038462                    0.17553   \n",
       "\n",
       "    a__binned_entropy__max_bins_10             ...               \\\n",
       "id                                             ...                \n",
       "1                         0.244930             ...                \n",
       "2                         0.990835             ...                \n",
       "3                         0.729871             ...                \n",
       "4                         1.322950             ...                \n",
       "5                         1.020037             ...                \n",
       "\n",
       "    f__fft_coefficient__coeff_0  f__fft_coefficient__coeff_1  \\\n",
       "id                                                             \n",
       "1                           0.0                     0.000000   \n",
       "2                          -4.0                     0.744415   \n",
       "3                          -4.0                    -0.424716   \n",
       "4                          -5.0                    -1.078108   \n",
       "5                          -2.0                    -3.743460   \n",
       "\n",
       "    f__fft_coefficient__coeff_2  f__fft_coefficient__coeff_3  \\\n",
       "id                                                             \n",
       "1                      0.000000                     0.000000   \n",
       "2                      1.273659                    -0.809017   \n",
       "3                      0.878188                     1.000000   \n",
       "4                      3.678858                    -3.618034   \n",
       "5                      3.049653                    -0.618034   \n",
       "\n",
       "    f__fft_coefficient__coeff_4  f__fft_coefficient__coeff_5  \\\n",
       "id                                                             \n",
       "1                      0.000000                          0.0   \n",
       "2                      1.373619                          0.5   \n",
       "3                      1.851767                          0.5   \n",
       "4                     -1.466977                         -0.5   \n",
       "5                      1.198375                         -0.5   \n",
       "\n",
       "    f__fft_coefficient__coeff_6  f__fft_coefficient__coeff_7  \\\n",
       "id                                                             \n",
       "1                      0.000000                     0.000000   \n",
       "2                      0.309017                    -1.391693   \n",
       "3                      1.000000                    -2.805239   \n",
       "4                     -1.381966                    -0.633773   \n",
       "5                      1.618034                    -0.004568   \n",
       "\n",
       "    f__fft_coefficient__coeff_8  f__fft_coefficient__coeff_9  \n",
       "id                                                            \n",
       "1                           0.0                          0.0  \n",
       "2                           0.0                          0.0  \n",
       "3                           0.0                          0.0  \n",
       "4                           0.0                          0.0  \n",
       "5                           0.0                          0.0  \n",
       "\n",
       "[5 rows x 1332 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 88 entries, 1 to 88\n",
      "Columns: 1332 entries, a__mean_abs_change_quantiles__qh_1.0__ql_0.8 to f__fft_coefficient__coeff_9\n",
      "dtypes: float64(1332)\n",
      "memory usage: 916.4 KB\n"
     ]
    }
   ],
   "source": [
    "X.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Feature Extraction: 100%|██████████| 6/6 [00:14<00:00,  2.43s/it]\n",
      "WARNING:tsfresh.utilities.dataframe_functions:The columns ['a__spkt_welch_density__coeff_8' 'c__spkt_welch_density__coeff_8'\n",
      " 'b__spkt_welch_density__coeff_8' 'e__spkt_welch_density__coeff_8'\n",
      " 'd__spkt_welch_density__coeff_8' 'f__spkt_welch_density__coeff_8'] did not have any finite values. Filling with zeros.\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature d__symmetry_looking__r_0.85 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature b__value_count__value_nan is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature f__mean_abs_change_quantiles__qh_0.2__ql_0.2 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature f__mean_abs_change_quantiles__qh_0.2__ql_0.4 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature f__value_count__value_inf is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature b__mean_abs_change_quantiles__qh_0.2__ql_0.8 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature b__mean_abs_change_quantiles__qh_0.2__ql_0.2 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature b__symmetry_looking__r_0.55 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature c__symmetry_looking__r_0.25 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature d__large_standard_deviation__r_0.05 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature b__symmetry_looking__r_0.85 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature e__mean_abs_change_quantiles__qh_0.8__ql_0.8 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature d__symmetry_looking__r_0.75 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature f__value_count__value_-inf is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature d__mean_abs_change_quantiles__qh_0.6__ql_0.6 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature c__mean_abs_change_quantiles__qh_0.6__ql_0.8 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature f__large_number_of_peaks__n_1 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature c__large_standard_deviation__r_0.15 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature b__symmetry_looking__r_0.3 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature c__large_standard_deviation__r_0.1 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature c__symmetry_looking__r_0.8 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature c__symmetry_looking__r_0.9 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature f__mean_abs_change_quantiles__qh_0.2__ql_0.6 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature c__symmetry_looking__r_0.3 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature c__symmetry_looking__r_0.0 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature b__mean_abs_change_quantiles__qh_0.6__ql_0.8 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature c__symmetry_looking__r_0.6 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature c__symmetry_looking__r_0.7 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature a__symmetry_looking__r_0.55 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature c__symmetry_looking__r_0.4 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature b__mean_abs_change_quantiles__qh_0.2__ql_0.6 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature c__symmetry_looking__r_0.5 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature f__large_number_of_peaks__n_3 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature b__symmetry_looking__r_0.4 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature b__symmetry_looking__r_0.6 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature d__mean_abs_change_quantiles__qh_0.2__ql_0.2 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature d__mean_abs_change_quantiles__qh_0.2__ql_0.8 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature b__symmetry_looking__r_0.0 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature c__value_count__value_inf is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature b__length is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature d__mean_abs_change_quantiles__qh_0.2__ql_0.6 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature d__mean_abs_change_quantiles__qh_0.2__ql_0.4 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature b__symmetry_looking__r_0.8 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature b__symmetry_looking__r_0.25 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature a__fft_coefficient__coeff_8 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature a__fft_coefficient__coeff_9 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature d__mean_abs_change_quantiles__qh_0.4__ql_0.4 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature f__fft_coefficient__coeff_9 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature c__fft_coefficient__coeff_8 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature c__fft_coefficient__coeff_9 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature f__index_mass_quantile__q_0.1 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature f__autocorrelation__lag_2 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature c__symmetry_looking__r_0.75 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature e__symmetry_looking__r_0.55 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature b__fft_coefficient__coeff_9 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature b__fft_coefficient__coeff_8 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature d__max_langevin_fixed_point__m_3__r_30 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature e__length is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature f__autocorrelation__lag_1 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature c__symmetry_looking__r_0.45 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature b__large_standard_deviation__r_0.15 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature a__symmetry_looking__r_0.85 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature f__mean_abs_change_quantiles__qh_0.2__ql_0.8 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature e__symmetry_looking__r_0.95 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature a__symmetry_looking__r_0.8 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature a__symmetry_looking__r_0.9 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature a__symmetry_looking__r_0.5 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature a__symmetry_looking__r_0.6 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature a__symmetry_looking__r_0.7 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature a__symmetry_looking__r_0.0 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature e__autocorrelation__lag_7 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature e__autocorrelation__lag_8 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature e__autocorrelation__lag_9 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature b__mean_abs_change_quantiles__qh_0.4__ql_0.8 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature c__mean_abs_change_quantiles__qh_0.4__ql_0.4 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature c__mean_abs_change_quantiles__qh_0.4__ql_0.6 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature b__mean_abs_change_quantiles__qh_0.6__ql_0.6 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature f__autocorrelation__lag_6 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature c__mean_abs_change_quantiles__qh_0.4__ql_0.8 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature b__value_count__value_-inf is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature c__mean_abs_change_quantiles__qh_0.8__ql_0.8 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature d__value_count__value_nan is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature b__symmetry_looking__r_0.7 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature e__symmetry_looking__r_0.8 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature e__symmetry_looking__r_0.9 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature e__value_count__value_nan is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature b__spkt_welch_density__coeff_8 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature e__symmetry_looking__r_0.5 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature f__autocorrelation__lag_4 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature e__symmetry_looking__r_0.6 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature e__symmetry_looking__r_0.7 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature e__friedrich_coefficients__m_3__r_30__coeff_0 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature e__friedrich_coefficients__m_3__r_30__coeff_1 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature f__friedrich_coefficients__m_3__r_30__coeff_2 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature c__large_standard_deviation__r_0.05 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature d__large_standard_deviation__r_0.0 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature e__symmetry_looking__r_0.0 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature b__symmetry_looking__r_0.9 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature b__symmetry_looking__r_0.75 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature a__symmetry_looking__r_0.65 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature a__length is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature e__spkt_welch_density__coeff_8 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature f__mean_abs_change_quantiles__qh_0.4__ql_0.4 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature e__large_standard_deviation__r_0.05 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature c__value_count__value_nan is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature c__value_count__value_-inf is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature b__symmetry_looking__r_0.5 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature f__autocorrelation__lag_9 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature e__mean_abs_change_quantiles__qh_0.2__ql_0.4 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature e__mean_abs_change_quantiles__qh_0.2__ql_0.6 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature c__length is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature c__large_standard_deviation__r_0.4 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature e__mean_abs_change_quantiles__qh_0.2__ql_0.2 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature e__mean_abs_change_quantiles__qh_0.2__ql_0.8 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature c__large_standard_deviation__r_0.2 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature b__large_standard_deviation__r_0.4 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature f__autocorrelation__lag_0 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature c__large_standard_deviation__r_0.0 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature b__large_standard_deviation__r_0.0 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature b__large_standard_deviation__r_0.1 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature c__mean_abs_change_quantiles__qh_0.6__ql_0.6 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature d__length is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature d__mean_abs_change_quantiles__qh_0.4__ql_0.6 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature a__large_standard_deviation__r_0.15 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature f__autocorrelation__lag_8 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature f__mean_abs_change_quantiles__qh_0.4__ql_0.6 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature f__mean_abs_change_quantiles__qh_0.6__ql_0.6 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature e__symmetry_looking__r_0.65 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature f__length is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature e__mean_abs_change_quantiles__qh_0.6__ql_0.6 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature c__symmetry_looking__r_0.85 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature f__index_mass_quantile__q_0.7 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature c__mean_abs_change_quantiles__qh_0.2__ql_0.4 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature b__autocorrelation__lag_9 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature b__value_count__value_inf is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature b__autocorrelation__lag_6 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature d__mean_abs_change_quantiles__qh_0.6__ql_0.8 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature b__autocorrelation__lag_5 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature c__symmetry_looking__r_0.55 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature e__symmetry_looking__r_0.35 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature b__mean_abs_change_quantiles__qh_0.4__ql_0.6 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature b__mean_abs_change_quantiles__qh_0.4__ql_0.4 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature a__symmetry_looking__r_0.95 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature c__friedrich_coefficients__m_3__r_30__coeff_2 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature f__mean_abs_change_quantiles__qh_0.8__ql_0.8 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature e__symmetry_looking__r_0.75 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature d__symmetry_looking__r_0.65 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature f__mean_abs_change_quantiles__qh_0.4__ql_0.8 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature e__symmetry_looking__r_0.85 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature c__symmetry_looking__r_0.95 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature a__large_standard_deviation__r_0.1 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature a__large_standard_deviation__r_0.0 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature a__large_standard_deviation__r_0.2 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature d__mean_abs_change_quantiles__qh_0.4__ql_0.8 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature e__mean_abs_change_quantiles__qh_0.6__ql_0.8 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature f__augmented_dickey_fuller is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature e__large_standard_deviation__r_0.1 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature e__large_standard_deviation__r_0.0 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature e__autocorrelation__lag_6 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature a__friedrich_coefficients__m_3__r_30__coeff_2 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature a__friedrich_coefficients__m_3__r_30__coeff_3 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature a__friedrich_coefficients__m_3__r_30__coeff_0 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature a__friedrich_coefficients__m_3__r_30__coeff_1 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature b__symmetry_looking__r_0.35 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature a__value_count__value_-inf is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature f__large_number_of_peaks__n_5 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature f__autocorrelation__lag_3 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature b__mean_abs_change_quantiles__qh_0.8__ql_0.8 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature f__autocorrelation__lag_7 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature f__autocorrelation__lag_5 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature b__large_standard_deviation__r_0.05 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature e__symmetry_looking__r_0.4 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature b__large_number_of_peaks__n_1 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature b__large_number_of_peaks__n_3 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature b__large_number_of_peaks__n_5 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature f__value_count__value_nan is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature d__large_standard_deviation__r_0.1 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature d__symmetry_looking__r_0.55 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature d__mean_abs_change_quantiles__qh_0.8__ql_0.8 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature b__max_langevin_fixed_point__m_3__r_30 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature d__symmetry_looking__r_0.95 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature d__large_standard_deviation__r_0.15 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature f__index_mass_quantile__q_0.8 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature e__large_standard_deviation__r_0.15 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature f__index_mass_quantile__q_0.9 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature f__index_mass_quantile__q_0.6 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature f__index_mass_quantile__q_0.4 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature b__symmetry_looking__r_0.45 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature f__index_mass_quantile__q_0.2 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature f__index_mass_quantile__q_0.3 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature a__autocorrelation__lag_9 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature f__max_langevin_fixed_point__m_3__r_30 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature e__max_langevin_fixed_point__m_3__r_30 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature a__symmetry_looking__r_0.75 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature a__large_number_of_peaks__n_3 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature a__large_number_of_peaks__n_1 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature a__large_number_of_peaks__n_5 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature a__value_count__value_inf is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature b__symmetry_looking__r_0.65 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature a__spkt_welch_density__coeff_8 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature e__value_count__value_inf is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature a__autocorrelation__lag_2 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature a__autocorrelation__lag_3 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature d__fft_coefficient__coeff_9 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature d__fft_coefficient__coeff_8 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature c__mean_abs_change_quantiles__qh_0.2__ql_0.8 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature c__mean_abs_change_quantiles__qh_0.2__ql_0.6 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature c__mean_abs_change_quantiles__qh_0.2__ql_0.2 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature a__mean_abs_change_quantiles__qh_0.8__ql_0.8 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature d__value_count__value_-inf is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature e__value_count__value_-inf is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature d__friedrich_coefficients__m_3__r_30__coeff_3 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature a__mean_abs_change_quantiles__qh_0.2__ql_0.8 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature d__friedrich_coefficients__m_3__r_30__coeff_2 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature d__friedrich_coefficients__m_3__r_30__coeff_1 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature d__friedrich_coefficients__m_3__r_30__coeff_0 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature a__symmetry_looking__r_0.45 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature d__value_count__value_inf is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature a__mean_abs_change_quantiles__qh_0.2__ql_0.2 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature b__symmetry_looking__r_0.95 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature a__mean_abs_change_quantiles__qh_0.2__ql_0.4 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature a__mean_abs_change_quantiles__qh_0.2__ql_0.6 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature e__friedrich_coefficients__m_3__r_30__coeff_2 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature e__friedrich_coefficients__m_3__r_30__coeff_3 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature f__symmetry_looking__r_0.0 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature e__fft_coefficient__coeff_8 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature e__fft_coefficient__coeff_9 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature c__large_number_of_peaks__n_5 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature a__value_count__value_nan is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature c__large_number_of_peaks__n_1 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature b__friedrich_coefficients__m_3__r_30__coeff_1 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature b__friedrich_coefficients__m_3__r_30__coeff_0 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature b__friedrich_coefficients__m_3__r_30__coeff_3 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature b__friedrich_coefficients__m_3__r_30__coeff_2 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature f__spkt_welch_density__coeff_8 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature c__large_standard_deviation__r_0.45 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature d__spkt_welch_density__coeff_8 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature b__autocorrelation__lag_8 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature a__max_langevin_fixed_point__m_3__r_30 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature e__symmetry_looking__r_0.45 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature a__mean_abs_change_quantiles__qh_0.6__ql_0.6 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature a__autocorrelation__lag_8 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature c__spkt_welch_density__coeff_8 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature c__symmetry_looking__r_0.65 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature a__mean_abs_change_quantiles__qh_0.6__ql_0.8 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature a__autocorrelation__lag_4 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature a__autocorrelation__lag_5 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature a__autocorrelation__lag_6 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature a__autocorrelation__lag_7 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature f__friedrich_coefficients__m_3__r_30__coeff_0 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature f__mean_abs_change_quantiles__qh_0.6__ql_0.8 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature e__mean_abs_change_quantiles__qh_0.4__ql_0.8 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature f__friedrich_coefficients__m_3__r_30__coeff_1 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature d__large_number_of_peaks__n_3 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature e__mean_abs_change_quantiles__qh_0.4__ql_0.6 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature f__friedrich_coefficients__m_3__r_30__coeff_3 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature c__max_langevin_fixed_point__m_3__r_30 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature e__mean_abs_change_quantiles__qh_0.4__ql_0.4 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature d__large_number_of_peaks__n_5 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature c__symmetry_looking__r_0.35 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature b__large_standard_deviation__r_0.45 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature f__fft_coefficient__coeff_8 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature c__friedrich_coefficients__m_3__r_30__coeff_3 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature d__symmetry_looking__r_0.9 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature d__symmetry_looking__r_0.8 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature b__autocorrelation__lag_7 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature c__friedrich_coefficients__m_3__r_30__coeff_0 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature a__large_standard_deviation__r_0.05 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature d__symmetry_looking__r_0.0 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature c__friedrich_coefficients__m_3__r_30__coeff_1 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature d__symmetry_looking__r_0.7 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature d__symmetry_looking__r_0.6 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature d__symmetry_looking__r_0.5 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature b__mean_abs_change_quantiles__qh_0.2__ql_0.4 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature a__mean_abs_change_quantiles__qh_0.4__ql_0.8 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature c__large_number_of_peaks__n_3 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature e__large_number_of_peaks__n_5 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature e__large_number_of_peaks__n_3 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature e__large_number_of_peaks__n_1 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature a__mean_abs_change_quantiles__qh_0.4__ql_0.6 is constant\n",
      "WARNING:tsfresh.feature_selection.feature_selector:[test_feature_significance] Feature a__mean_abs_change_quantiles__qh_0.4__ql_0.4 is constant\n"
     ]
    }
   ],
   "source": [
    "X_filtered = extract_relevant_features(df, y, \n",
    "                                       column_id='id', column_sort='time', \n",
    "                                       default_fc_parameters=extraction_settings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a__abs_energy</th>\n",
       "      <th>a__range_count__max_1__min_-1</th>\n",
       "      <th>b__abs_energy</th>\n",
       "      <th>e__variance</th>\n",
       "      <th>e__standard_deviation</th>\n",
       "      <th>e__abs_energy</th>\n",
       "      <th>c__standard_deviation</th>\n",
       "      <th>c__variance</th>\n",
       "      <th>a__standard_deviation</th>\n",
       "      <th>a__variance</th>\n",
       "      <th>...</th>\n",
       "      <th>b__has_duplicate_max</th>\n",
       "      <th>b__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_14__w_5</th>\n",
       "      <th>b__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_13__w_2</th>\n",
       "      <th>e__quantile__q_0.1</th>\n",
       "      <th>a__ar_coefficient__k_10__coeff_1</th>\n",
       "      <th>a__quantile__q_0.2</th>\n",
       "      <th>b__quantile__q_0.7</th>\n",
       "      <th>f__large_standard_deviation__r_0.35</th>\n",
       "      <th>f__quantile__q_0.9</th>\n",
       "      <th>d__spkt_welch_density__coeff_5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.471405</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.203698</td>\n",
       "      <td>1.448889</td>\n",
       "      <td>0.249444</td>\n",
       "      <td>0.062222</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.751682</td>\n",
       "      <td>-0.310265</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.037795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>4.222222</td>\n",
       "      <td>2.054805</td>\n",
       "      <td>90.0</td>\n",
       "      <td>4.333846</td>\n",
       "      <td>18.782222</td>\n",
       "      <td>0.956847</td>\n",
       "      <td>0.915556</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.057818</td>\n",
       "      <td>-0.202951</td>\n",
       "      <td>-3.6</td>\n",
       "      <td>-0.078829</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.319311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>3.128889</td>\n",
       "      <td>1.768867</td>\n",
       "      <td>103.0</td>\n",
       "      <td>4.616877</td>\n",
       "      <td>21.315556</td>\n",
       "      <td>0.596285</td>\n",
       "      <td>0.355556</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.912474</td>\n",
       "      <td>0.539121</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>0.084836</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.102780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>7.128889</td>\n",
       "      <td>2.669998</td>\n",
       "      <td>124.0</td>\n",
       "      <td>3.833188</td>\n",
       "      <td>14.693333</td>\n",
       "      <td>0.952190</td>\n",
       "      <td>0.906667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.609735</td>\n",
       "      <td>-2.641390</td>\n",
       "      <td>-4.6</td>\n",
       "      <td>0.003108</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56.910262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>17.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>4.160000</td>\n",
       "      <td>2.039608</td>\n",
       "      <td>180.0</td>\n",
       "      <td>4.841487</td>\n",
       "      <td>23.440000</td>\n",
       "      <td>0.879394</td>\n",
       "      <td>0.773333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.072771</td>\n",
       "      <td>0.591927</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>0.087906</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>22.841805</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 300 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    a__abs_energy  a__range_count__max_1__min_-1  b__abs_energy  e__variance  \\\n",
       "id                                                                             \n",
       "1            14.0                           15.0           13.0     0.222222   \n",
       "2            25.0                           13.0           76.0     4.222222   \n",
       "3            12.0                           14.0           40.0     3.128889   \n",
       "4            16.0                           10.0           60.0     7.128889   \n",
       "5            17.0                           13.0           46.0     4.160000   \n",
       "\n",
       "    e__standard_deviation  e__abs_energy  c__standard_deviation  c__variance  \\\n",
       "id                                                                             \n",
       "1                0.471405           10.0               1.203698     1.448889   \n",
       "2                2.054805           90.0               4.333846    18.782222   \n",
       "3                1.768867          103.0               4.616877    21.315556   \n",
       "4                2.669998          124.0               3.833188    14.693333   \n",
       "5                2.039608          180.0               4.841487    23.440000   \n",
       "\n",
       "    a__standard_deviation  a__variance               ...                \\\n",
       "id                                                   ...                 \n",
       "1                0.249444     0.062222               ...                 \n",
       "2                0.956847     0.915556               ...                 \n",
       "3                0.596285     0.355556               ...                 \n",
       "4                0.952190     0.906667               ...                 \n",
       "5                0.879394     0.773333               ...                 \n",
       "\n",
       "    b__has_duplicate_max  \\\n",
       "id                         \n",
       "1                    1.0   \n",
       "2                    1.0   \n",
       "3                    0.0   \n",
       "4                    0.0   \n",
       "5                    0.0   \n",
       "\n",
       "    b__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_14__w_5  \\\n",
       "id                                                              \n",
       "1                                           -0.751682           \n",
       "2                                            0.057818           \n",
       "3                                            0.912474           \n",
       "4                                           -0.609735           \n",
       "5                                            0.072771           \n",
       "\n",
       "    b__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_13__w_2  \\\n",
       "id                                                              \n",
       "1                                           -0.310265           \n",
       "2                                           -0.202951           \n",
       "3                                            0.539121           \n",
       "4                                           -2.641390           \n",
       "5                                            0.591927           \n",
       "\n",
       "    e__quantile__q_0.1  a__ar_coefficient__k_10__coeff_1  a__quantile__q_0.2  \\\n",
       "id                                                                             \n",
       "1                 -1.0                          0.125000                -1.0   \n",
       "2                 -3.6                         -0.078829                -1.0   \n",
       "3                 -4.0                          0.084836                -1.0   \n",
       "4                 -4.6                          0.003108                -1.0   \n",
       "5                 -5.0                          0.087906                -1.0   \n",
       "\n",
       "    b__quantile__q_0.7  f__large_standard_deviation__r_0.35  \\\n",
       "id                                                            \n",
       "1                 -1.0                                  0.0   \n",
       "2                 -1.0                                  1.0   \n",
       "3                  0.0                                  1.0   \n",
       "4                  1.0                                  0.0   \n",
       "5                  0.8                                  0.0   \n",
       "\n",
       "    f__quantile__q_0.9  d__spkt_welch_density__coeff_5  \n",
       "id                                                      \n",
       "1                  0.0                        0.037795  \n",
       "2                  0.0                        0.319311  \n",
       "3                  0.0                        9.102780  \n",
       "4                  0.0                       56.910262  \n",
       "5                  0.6                       22.841805  \n",
       "\n",
       "[5 rows x 300 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_filtered.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 88 entries, 1 to 88\n",
      "Columns: 300 entries, a__abs_energy to d__spkt_welch_density__coeff_5\n",
      "dtypes: float64(300)\n",
      "memory usage: 206.9 KB\n"
     ]
    }
   ],
   "source": [
    "X_filtered.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, X_filtered_train, X_filtered_test, y_train, y_test = train_test_split(X, X_filtered, y, test_size=.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      0.89      0.94         9\n",
      "          1       0.96      1.00      0.98        27\n",
      "\n",
      "avg / total       0.97      0.97      0.97        36\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cl = DecisionTreeClassifier()\n",
    "cl.fit(X_train, y_train)\n",
    "print(classification_report(y_test, cl.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1332"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cl.n_features_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      0.78      0.88         9\n",
      "          1       0.93      1.00      0.96        27\n",
      "\n",
      "avg / total       0.95      0.94      0.94        36\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cl2 = DecisionTreeClassifier()\n",
    "cl2.fit(X_filtered_train, y_train)\n",
    "print(classification_report(y_test, cl2.predict(X_filtered_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "300"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cl2.n_features_"
   ]
  }
 ],
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